Interfaces between biological and computing systems are becoming more commonplace, particularly in application areas of assistive rehabilitation and movement aids, such as powered prosthetic devices. The vast majority of these devices use state machine architectures, state definitions being comprised of one or more pre-defined pattern templates to translate between biological function and control algorithms. The state definitions used for these purposes, however, are necessarily idealized or averaged from a broad population. They therefore are invariably in any instance imperfect replicas of the underlying biological elements, which vary tremendously from individual to individual in differing conditions. Beyond these individual differences, environmental conditions, clothing, sensor positions, specific movements of an action, and motion speed are but a few of the many variables that can change extremely quickly, frustrating operation within any pattern template. To accommodate all variances, physical position and velocity patterns used must often then be so broad as to negatively impact even operation in slow-moving contexts. Further performance degradation in quickly-moving dynamic situations usually results when smaller positional changes, which occur more often at higher speeds, fall well within the broad template positional ranges mandated.
Adaptation of idealized state definition templates to an individual instance through selective parameter modification has shown moderate success, most notably improving transient behavior by tightening state boundary definitions. Even when dynamically optimized, however, direct comparison of measured conditions against any state definition template inherently limits viability to those conditions defined within the template. Inherently lacking extrapolation, predictable system control in a purely template-driven system occurs only when input conditions fall within template definitions. This therefore constrains tight control operation to circumstances in which the state definition templates have been either defined or trained. For example, dynamic adaptation which forms a template to define dynamically-stimulated leg muscle contraction criteria during normal walking on a level surface does not necessarily extend to previously unencountered situations, such as descending stairs. In the presence of poorly-defined states, direct measurement of the end control goal, particularly in biological applications, is therefore extremely difficult at best, making classic feedback loops untenable.
Following the leg stimulation example, the purpose of an orthopedic device may be to protect a damaged joint through external dynamically controlled force vectors. Implicit to this goal is maintaining internal vectored joint force magnitudes within proscribed limits. Due to inertial components in the multiple degrees of freedom involved, similar vectored forces within the joint may however result from a great number of disrelated positional and motion conditions. External position and motion measurements show poor correlation with internal vectored joint force magnitudes, primarily due to the compound nature of most joints. Direct control loop closure on internal joint forces is made untenable by joint invasion in most applications. For example, it may be impractical to calculate internal condyle forces by physically placing load cells inside the joint to get internal force measurements. Yet development of a positional template with high coverage of all scenarios requiring vectored joint protection is made unlikely by the sheer number of positions and motions possible. The previous example depicts a scenario wherein nonlinear behavior of a controlled system is not readily apparent through direct measurements, but is available in concise form through modeling of the physical system, as excited by measured dynamic conditions. This concise form is highly amenable to a state machine architecture, not only in biological interfaces like the example above, but in any control situation wherein direct control of higher-order effects of a multiplicity of excitation sources is desired.
Computational requirements of a state machine are in direct proportion to the number of state definitions inspected in any period of time. Highest efficiency then occurs with a minimum number of state exit/entrance criteria open for inspection at any given time. Significant control events, however, such as bone-on-bone condular impact in the orthopedic example above, often exist in a context of variances both in time and from instance to instance. In the example above, gait patterns predictive of condular impact vary significantly both with patient fatigue and from patient to patient. With broad physical system variances, identities of measured and even modeled conditions indicative of a significant control event can change or even be unknown when the control system is designed. Following the orthopedic example, knee kinematics are very different between heavy and slight patients. In this and many other examples, lack of extrapolative capability mandates that optimal state template definitions exhaustively cover possible system instances and conditions. A broad set of state condition criteria which covers all applications is not only computationally inefficient, but loses ability to distinguish by averaging the context in which significant control events are inspected.
A need exists for a state determination technique with adequate knowledge of the underlying structural system to allow robust and accurate control operation in both previously-observed and completely new operational conditions.